2 citations · 8 across the 12 of their papers we have counts for
10 papers · 1 filter
On tail-robust autocovariance matrix estimation for high-dimensional and potentially nonstationary time series
Haotian Xu, Stephane Guerrier, Runze Li +1
In this paper, we study the autocovariance matrix estimation and inference problems under heavy-tailedness, high-dimensionality, general nonlinear temporal dependence, and potentia…
Accurate Inference for Penalized Logistic Regression
Yuming Zhang, Stéphane Guerrier, Runze Li
Inference for high-dimensional logistic regression models using penalized methods has been a challenging research problem. As an illustration, a major difficulty is the significant…
Inference for Large Scale Regression Models with Dependent Errors
Lionel Voirol, Haotian Xu, Yuming Zhang +3
The exponential growth in data sizes and storage costs has brought considerable challenges to the data science community, requiring solutions to run learning methods on such data.…
An accurate percentile method for parametric inference based on asymptotically biased estimators
Samuel Orso, Mucyo Karemera, Maria-Pia Victoria-Feser +1
Inference methods for computing confidence intervals in parametric settings usually rely on consistent estimators of the parameter of interest. However, it may be computationally a…
Accounting for Vibration Noise in Stochastic Measurement Errors
Lionel Voirol, Davide A. Cucci, Mucyo Karemera +3
The measurement of data over time and/or space is of utmost importance in a wide range of domains from engineering to physics. Devices that perform these measurements therefore nee…
Prevalence Estimation from Random Samples and Census Data with Participation Bias
Stéphane Guerrier, Christoph Kuzmics, Maria-Pia Victoria-Feser
Countries officially record the number of COVID-19 cases based on medical tests of a subset of the population with unknown participation bias. For prevalence estimation, the offici…